353 results about "Constant false alarm rate" patented technology

Systems and methods using constant false alarm rate techniques for event detection. One example of an event detection method includes collecting a first distribution of measurements for a first physiological parameter. In another example, the first distribution of measurements includes only non-event measurements. One or more values are determined corresponding to at least a first tail area of the first distribution from at least one measurement of the first physiological parameter toward an end point of the distribution. A threshold is established based on a specified false alarm rate. The one or more values are compared to the threshold. The method includes determining if the measurement is representative of a detected event using the comparison.

The invention relates to a radar moving target Radon-fractional Fourier transform (RFRFT) long-time phase-coherent accumulation detection method, and belongs to the technical field of radar signal processing and detection. The radar moving target Radon-fractional Fourier transform long-time phase-coherent accumulation detection method includes steps of 1), performing range demodulation and pulse pressure for radar echo to complete intra-pulse accumulation; 2), initializing parameters; 3), completing long-time inter-pulse phase-coherent accumulation by RFRFT compensation distance and Doppler frequency migration; 4), traversing all search parameters and creating a distance-RFRFT domain detection unit graph; 5), performing constant false alarm rate detection for the detection unit graph; and 6), estimating movement parameters of a target and outputting movement point traces. The radar moving target Radon-fractional Fourier transform long-time phase-coherent accumulation detection method has the advantages that amplitude information and phase information of the echo of the moving target are simultaneously utilized for long-time phase-coherent accumulation, the distance and the Doppler frequency migration in a long-time accumulation procedure are compensated, background clutter and noise are effectively suppressed, an accumulation gain is increased, dim moving targets in the heavy cluster can be detected, the movement point traces of the target can be acquired, and the method has popularization and application value.

The invention discloses a CFAR (constant false alarm rate) detection and depth learning-based SAR (synthetic aperture radar) target detection method. The method includes the following steps that: (1) SAR images are obtained; (2) a training sample set is expanded; (3) the network structure of a Faster-RCNN model is constructed; (4) a trained RPN model is obtained; (5) a trained Fast-RCNN model is obtained; (6) a finely-adjusted RPN network is obtained; (7) a trained Faster-RCNN model is obtained; and (8) target detection is carried out. With the CFAR (constant false alarm rate) detection and depth learning-based SAR (synthetic aperture radar) target detection method of the invention adopted, end-to-end image-level detection is realized, and major problems in existing SAR target detection technologies can be solved. The method has good detection performance under complex conditions.

By embedding as a Physical Layer (PHY) Appliqué patterns (time, frequency, tone multiplexed, control signal, explicit network authentication, projective, other, or combined) on wireless electronic communications network (WECN) digital signal packets, and using a QRD-based auto-SCORE adaptation algorithm to maximize the signal-to-interference-and-noise ratio (SINR) attainable by multielement arrays over very small time-bandwidth products, differentiation and detection of signals from environmental noise (particularly from other or non-network signals) can be improved, allowing more compressed, secure, efficient network operations. By projecting the signal embedding onto a known linear subspace and using integrated transmit/receive structures, packet detection algorithms, and blind array adaptation algorithms, the WECN processes the data received with constant false-alarm rate, generalized, maximum-likelihood detectors and multilink signal estimation algorithms, based only on knowledge of the embedding pattern used at the transmit node. The resultant system provides PHY information-assured (PHY-IA) data transmission with improved network efficiency and security.

The invention discloses a real-time processing system for radar signals of an outer radiation source based on a GPU (Graphics Processing Unit) and a processing method. The invention mainly solves the problem that conventional technologies are slow in speed and high in development expenses. The system comprises an octo-array element antenna, a data collection unit, a digital channelized receiving unit, a data transmission unit, a data processing unit and a terminal display and control unit. The data collection unit performs amplification, sampling, A/D (Analogue to Digital) conversion and down-conversion for eight signals of a frequency modulation broadcasting base station received by the octo-array element antenna and receives the signals by the digital channelized receiving unit in a digitalized manner; then, the received signals transmitted by the data transmission unit via an Ethernet card to the data processing unit for digital beam forming, clutter cancellation, distance-Doppler two-dimensional correlation, constant false alarm rate detection and amplitude comparison angle detection. The terminal display and control unit performs target infusion and track treatment for the processing result to obtain a target position. According to the real-time processing system for the radar signals of the outer radiation source based on the GPU and the processing method provided by the invention, the data processing speed is fast, and real-time processing of the radar signals of the outer radiation source can be realized with lower cost.

The invention discloses a segmentation combination-based adaptive constant false alarm rate target detection method for an SAR (synthetic aperture radar) image, and belongs to the technical field of synthetic aperture radars. The method comprises the following steps of: dividing a reference window into four sub windows, extracting uniformity statistics of the four sub windows and judging whether the sub windows are uniform; obtaining parameters for estimating background clutter models by adopting corresponding sub window combination strategies according to the non-uniform number, and then obtaining a detection threshold value by using a false alarm probability and a relationship between the clutter models; and comparing the pixel value of a current detection unit with the detection threshold value, judging whether a target exists, detecting the whole SAR image to be detected by adopting a running water form, and performing target fusion operation on the detected SAR image. According to the scheme, the method has low calculation quantity and simple operation, solves the problems of low detection probability, high false alarm rate and the like when the environment is complex and changeable and multiple targets are adjacent under high-resolution large scenes in the prior art, obviously improves the detection effect, and can keep good detection performance under various complex detection environments.

The invention discloses a high-dynamic weak-signal rapid capture method for a direct sequence spread spectrum system, belonging to the field of radio communication. Because a spread spectrum carrier has a chirp signal characteristic under the condition of high-dynamic motion (high-speed and high-acceleration), the high-dynamic weak-signal rapid capture method comprises the steps of: firstly, carrying out carrier Doppler frequency compensation by using a time frequency focusing characteristic of fractional order Fourier transform; secondly, carrying out incoherent accumulation on a spread frequency signal by using an order resolving capacity of the fractional order Fourier transform; and finally, carrying out capture judgment on the signal in an order Fourier domain by using a constant false alarm rate detection technology. According to the invention, the difficulty of incapability of long-time coherent accumulation under the high-dynamic condition in the traditional Fourier transform based rapid capture method is solved; and under the condition of high dynamicity and low signal to noise ratio, the signal to noise ratio is effectively increased and the signal capture time is shortened. In addition, a rapid algorithm exists in the invention and is easy to realize on the engineering in real time.

The invention discloses a fast high-resolution SAR (synthetic aperture radar) image ship detection method based on feature fusion and clustering. The fast high-resolution SAR image ship detection method comprises the following steps: on the basis of the back scattering characteristics of each ground object and the prior information of a ship target in an SAR image, positioning a target potential position index map by an Otsu algorithm and range constraint; on the index map, pre-screening to obtain a detection binary segmentation map by a CFAR (constant false alarm rate) algorithm based on a local contrast; carrying out morphological processing to a detection result, and extracting a potential target slice from the SAR image and a detected binary segmentation map according to a processing result; and carrying out K-means clustering to the extracted slice by a designed identification feature to obtain a final identification result. According to the fast high-resolution SAR image ship detection method based on feature fusion and clustering, the data volume of a detection stage is effectively reduced by pre-processing, and point-to-point detection is not needed/the time of point-to-point detection is saved. Meanwhile, a target identification problem under the condition of insufficient training samples at present can be solved by the designed characteristic and a non-supervision clustering method, the target can be effectively positioned, and the size of the target can be estimated.

The invention discloses a method and a system for resisting dense forwarding type defraud interference of an airborne radar, and the resistance of dense forwarding type defraud interference is realized through an interference scout module and an interference filter and elimination module. Interference scout is used for carrying out the PD (probability of defraud) treatment on data received by a radar; the CFAR (constant false alarm rate) detection is carried out on data in a clear area; a detection result direction of arrival is estimated; an interference direction of arrival is estimated with combination of the detection result of a plurality of wave positions. In the interference filter and elimination treatment, a sum beam pointing to the interference direction is formed according to an interference scout result; the sum beam pointing to the interference direction is used as an auxiliary passageway, an existing space passageway is used as a main passageway, the method of GSC (gain scheduling control) is adopted to filter and eliminate interference, and the channel covariance matrix of the interference is estimated according to an interference sample selected from the clear area. By adopting the method and the system, the difficult problem of inhibition on dense forwarding type defraud interference is solved, the dense forwarding type interference is fundamentally filtered and eliminated, false alarms caused by the dense forwarding type interference are reduced, adverse effects on a CRAR detection threshold and the STAP (space time adaptive processing) performance are eliminated, the detection performance of a radar target is improved, the system is simple and easy to realize, and an engineering application value is provided.

The present invention discloses an SAR (Synthetic Aperture Radar) image target detection method based on a visual attention model and a constant false alarm rate, which mainly solves the problems of a low detection speed and a high clutter false alarm rate in the existing SAR image marine ship target detection technology. The implementation steps of the method are as follows: extracting a saliency map corresponding to an SAR image according to Fourier spectrum residual error information; calculating a saliency threshold, so as to select a potential target area on the saliency map; detecting the potential target area by adopting an adaptive sliding window constant false alarm rate method, and obtaining an initial detection result; and obtaining a final detection result after removing a false alarm from the initial detection result, and extracting a suspected ship target slice, so as to complete a target detection process. The SAR image target detection method based on the visual attention model and the constant false alarm rate provided by the present invention has the advantages of a high calculation speed, a high target detection rate and a low false alarm rate, and meanwhile the method has the advantages of simpleness and easy implementation and can be used for marine ship target detection.

The invention discloses a CFAR (Constant False Alarm Rate) and sparse representation-based high-resolution SAR (Synthetic Aperture Radar) image ship detection method, which mainly solves the problems of large data quantity to be processed and low real-time property existing in the conventional method. The method comprises the following detection steps: selecting a ship target training sample in a high-resolution SAR image and determining the size of a CFAR sliding window by the training sample; down-sampling the high-resolution image, performing image segmentation and land elimination on the high-resolution image, detecting in a low-resolution image by using the CFAR method and performing preliminary identification, and mapping a detected pixel point to a potential target region in the original high-resolution image; outputting potential target region slices obtained by all detection; and finally, extracting characteristic vectors of the potential target region slices respectively and identifying through a sparse representation classifier to obtain a final ship detection result. The CFAR and sparse representation-based high-resolution SAR image ship detection method has the advantages of high detection speed, high detection rate and low false alarm rate, and can be used for fishery supervision, maritime safety management and the like.

The invention belongs to the radar target detection technology field and relates to a radar target constant false alarm rate detection method. The radar target constant false alarm rate detection method mainly comprises the following steps: 1) the Weibull background distribution is converted to the Location-Scale distribution through the logarithmic transformation; 2) the orderly truncated (or head cut) treatment is carried out to a background sample to suppress the impact of an interference target, a largest unbiased estimator is adopted for estimating the location and the scale parameters; 3) the normalization of samples in a unit to be tested is carried out by using the estimated parameters; 4) the judgment of whether the background distribution is obeyed or not is carried out by utilizing the Anderson-Darling test, if the background distribution is not obeyed, the target is judged to exist, otherwise, no target is judged to exist. The radar target constant false alarm rate detection method utilizes the difference of the distribution characteristics of the target echo and the background clutter, compared with the traditional detection method based on a self-adaptive threshold, the characteristics of the background distribution and the interference target have little impact on the radar target constant false alarm rate detection method, thereby having very strong adaptability to the non-Gaussian environment and the multi-objective interference environment.

Te invention discloses a two-dimensional self-adaptive radar CFAR (constant false alarm rate) detection method, and mainly solves reduced detection performance during detection by using a signle CA_CFAR method and a signle OS_CFAR method when multiple targets or strong interference occur in clutter background. The method is realized through the following steps of 1) dividing a M*N clutter matrix block following different distributions into n p*q sub-blocks; 2) calculating a judgement factor alpha of an attribute of every sub-block; 3) judging the attribute of every sub-block according to the judgement factor alpha; 4) calculating a two-dimensional unit average CFAR detection threshold value factor T1 and a two-dimensional ordered CFAR detection threshold value factor T2 in different clutter distribution conditions; 5) obtaining detection thresholds K1 (i, j) and K2 (i, j) of every data unit of uniform distributed sub-blocks and non-uniform distributed sub-blocks respectively by using the threshold value factors T1 and T2; and 6) comparing the detection thresholds with every data unit during radar target detection, thereby determining whether a target exists in the data units. The method has the advantages of high detection performance and strong capability of coping with complex environments.

The invention belongs to the technical field of target track generation, particularly relates to an outer transmitter-based radar target track processing method based on clustering. The outer transmitter-based radar target track processing method based on clustering includes the steps of firstly, receiving the signal of each transmitter through a receiving antenna, and conducting channel balancing and down-conversion on the signal of each transmitter to obtain original data; secondly, sequentially conducting clutter cancellation, distance-Doppler operation, constant false alarm rate detection and amplitude comparison angle measurement on the original data so as to obtain front end processed data; thirdly, generating the tracks of all targets through a clustering method according to the front end processed data.

The invention discloses a constant false alarm rate detection method adaptive to environmental changes. Correlation of corresponding positions of multiple periodic data and correlation of adjacent data of the same group of data are used for threshold detection, that is, time correlation and space correlation are used for threshold detection, and an interference memory group is initialized respectively to be spectrum information of first periodic data through an initialized part; clutter information of the current data is estimated to be a result when clutter information in former M periods and the spectrum information of the current data are weighted; if the located environment changes greatly or quickly, the weight of the current data information on clutter estimation can be properly increased; and the currently-estimated clutter information is used for setting of a later constant false alarm rate detection threshold. According to the method of the invention, in homogeneous background, a unit average constant false alarm rate is used to achieve optimal performance, and in nonhomogeneous background, an ordered statistical method is used for enabling the detection performance and the false alarm performance to be significantly improved.

The invention discloses a self-adaption constant false alarm rate (CFAR) fusion detection method aiming at a multi-target background radar, belonging to the field of radar signal processing. Under the background that the number of interference targets is unknown and for purpose of solving the problems that the existing CFAR detection method causes the reduction of detection performances or overhigh calculation complexity, the invention provides a simple and effective self-adaption CFAR fusion detection method. The method comprises the steps: automatically determining the number of interference targets according to Gaussian distributed 3-Sigma rules; based on the evaluated number of the interference targets, effectively rejecting an interference target sample in a reference unit, removing the adverse influence of the interference targets, and fusing the remaining pure clutter sampling value to form a detection threshold. The method can be used for detecting multiple targets while meeting the CFRA, has strong false alarm control capability and multi-target resolution capability, is simple to calculate and convenient for engineering realization, and has popularization and application values.

The present invention relates to a system for using signals scattered by targets to determine position and velocity for each of the targets and comprises a set of transmitters and receivers of electromagnetic or acoustic signals, said transmitters and receivers dispersed to known points. Each pair of transmitter and receiver, monostatic or bistatic, is named a measuring facility. The ranges of the transmitters are chosen so that a target at an arbitrary point within the position space can be measured via scattering in the target by at least four measuring facilities. For each measuring facility, target detection occurs with constant false alarm rate in the form of probabilities over resolution cells with regards to range and Doppler velocity and conceivable targets are placed in a 2-dimensional linear space belonging to the measuring facility. The 3-dimensional positions and 3-dimensional Doppler velocities are represented as a 6-dimensional linear position and velocity space subdivided into resolution cells with the same resolution of range and Doppler velocity that is found at the measuring facilities. For each intersection representing detections at at least four measuring facilities the probability is calculated that the intersection is a false alarm emanating intersections between subsets from different targets and when the probability falls below a predefined value, it is given that the intersection contains at least one target. The target positions and target velocities are extracted in this way.”

The invention relates to a self-adaptive constant false alarm rate target detection method. The method includes the following steps: 1), transmitting data received by radar into a matching filter; 2), transmitting signals output by the matching filter into a square-law detector for processing; 3), transmitting signals output by the square-law detector into a CFAR (constant false alarm rate) detector for processing, and acquiring an estimation value Z of cluster power level generated by a reference unit according to a corresponding CFAR algorithm through sampling; 4), outputting a final judgment by the CFAR detector according to the estimation value Z acquired in step 3), namely detecting whether a target exists in the unit or not. In the method, according to statistical average value and variance of sampling values in a reference sliding window, the sampling value with the variance larger than a certain numerical value is deleted, an average value of remaining valid sampling values is used to replace the sampling value, and an average value of the sampling values is recalculated.

The invention belongs to the technical field of radar signal processing, and discloses an object plot clotting method of radar echo data. The method includes the following steps: acquiring original echo data of a plurality of scanning periods of a radar; successively conducting multi-pulse incoherent integration on the original echo data, obtaining echo data after pulse integration; conducting constant false alarm rate detection on the echo data which is subject to pulse integration, successively conducting binary quantization on the echo data which is subject to constant false alarm rate detection and conducting binary sliding window detection along the direction of a direction-fractal dimension; the echo data which is subject to binary sliding window serving as binary image data, firstly inflating the binary image data and then corroding the binary image data so as to obtain all connected domain of the binary image data; filtering a fist connected domain and a second connected domain, obtaining residual connected domain containing object plots; on the basis of the residual connected domain which contains the object plots, obtaining object information, realizing clotting of the object plots. The method can increase object parameter estimation precision and object resolution capability.

The invention discloses an adaptive intelligent integration detection method of a radar range extension target and belongs to the radar signal processing field. The adaptive intelligent integration detection method of the radar range extension target aims at overcoming the defects that the existing range extension target statistical detection method is poor in adaptive capability to the clutter and target environment and the like under the condition of the unknown clutter background. According to the adaptive intelligent integration detection method of the radar range extension target, normalized processing is performed on a radar echo one-dimensional range profile, integration optimization is performed through data domain description characteristics of a clutter one-dimensional range profile, a detection method of the constant false alarm rate of a one-class support vector machine is established, a kernel function parameter is adjusted to ensure constant false alarm rate characteristics of the detection method, and a clutter database is updated according to a detection result; statistical modeling does not need to be performed on the clutter background; the detection method is suitable for the positive and negative sample quantity seriously asymmetrical radar target detection environment; the false alarm control capability is strong, the computation is less, the engineering implementation is convenient, and the popularization and application value is high.

The invention provides an integral image-based quick ACCA-CFAR SAR (Automatic Censored Cell Averaging-Constant False Alarm Rate Synthetic Aperture Radar) image target detection method, comprising the following steps of: (1) providing a G0 distribution-based self-adaptive global threshold CFAR pre-segmentation algorithm used for generating a target index matrix by combining the statistical property of data; (2) providing an integral image-based G0 distribution statistical parameter quick estimation method, wherein the statistical parameter can be calculated through simple operations such as addition and subtraction once 2-order and 4-prder integral images of an original image are obtained during the implementation of the method; and (3) giving out a basic implementation process of the ACCA-CFAR SAR image target detection method. Through the integral image-based G0 distribution statistical parameter quick estimation strategy provided by the invention, the time efficiency of the method can be greatly improved and the time complexity of the method is irrelevant to the size of a sliding window; and the requirement of the existing automatic target recognition (ATR) system on the treatment of large-scene data can be met to a great extent.

The invention discloses a hyperspectral abnormal object detection method based on structure sparse representation and internal cluster filtering, aiming at addressing the technical problem of low object detection effciency of current hyperspectral abnormal object detection methods. The technical solution involves: after selecting an initial background pixel, using the dictionary learning method which is based on principal component analysis to study a background dictionary which obtains rebustness, in the course of sparse vector resolution and image reconstruction, introducing re-weighted laplacian prior to increase the solution precision of sparse vector, computing the errors betwen an original image and a reconstructed image to obtain a sparse representation error, using the internal cluster filtering to represent space spectrum characteristics of hyperspectral data, obtaining the internal cluster error by computing the error between a to-be-tested pixel and other pixel linear representation result, and finally combining the sparse representation error and the linear weighting of the internal cluster error and implementing precise extraction of an abnormal object. According to the invention, the method increases 10-15% of detection rate with the proviso of a constant false alarm rate compared with prior art.

The invention relates to a method for detecting a spread target based on phase compensation processing. The method comprises the following steps of: 1, producing a high-resolution one-dimensional range image of the spread target, namely performing matched filtering on a linear frequency-modulated wave signal of a high-resolution radar to acquire the one-dimensional range image of the spread target; 2, performing the phase compensation processing on an echo sequence of the spread target; 3, accumulating energy of the spread target, namely performing in-phase stacking on the amplitude of strongscattering point signals in the one-dimensional range image of the spread target; and 4, detecting the spread target by using a cell average-constant false alarm rate (CA-CFAR) detector. In the process, all types of work aim to enhance the detection capability of the spread target so as to correctly detect the spread target.